import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
from datetime import datetime

x_data = np.linspace(-0.5, 0.5, 200)[:, np.newaxis]
noise = np.random.normal(0, 0.02, x_data.shape)
y_data = np.square(x_data) + noise

x = tf.placeholder(tf.float32, [None, 1])
y = tf.placeholder(tf.float32, [None, 1])

Weights_L1 = tf.Variable(tf.random_normal([1, 10]))
biases_L1 = tf.Variable(tf.zeros([1, 10]))
Wx_plus_b_L1 = tf.matmul(x, Weights_L1) + biases_L1
L1 = tf.nn.tanh(Wx_plus_b_L1)

Weights_L2 = tf.Variable(tf.random_normal([10, 1]))
biases_L2 = tf.Variable(tf.zeros([1, 1]))
Wx_plus_b_L2 = tf.matmul(L1, Weights_L2) + biases_L2

prediction = tf.nn.tanh(Wx_plus_b_L2)

loss = tf.reduce_mean(tf.square(y - prediction))

train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    a = datetime.now()
    for _ in range(20000):
        sess.run(train_step, feed_dict={x: x_data, y: y_data})
    b = datetime.now()
    print("总耗时 " + str((b - a).seconds) + " 秒")
    prediction_value = sess.run(prediction, feed_dict={x: x_data})

    plt.figure()
    plt.scatter(x_data, y_data)
    plt.plot(x_data, prediction_value, 'r-', lw=2)
    plt.show()
